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Merge pull request #501 from WenjieDu/dev
Add TimeMixer
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| The package including the modules of TimeMixer. | ||
| Refer to the paper | ||
| `Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y. Zhang, and Jun Zhou. | ||
| "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting". | ||
| In ICLR 2024. | ||
| <https://openreview.net/pdf?id=7oLshfEIC2>`_ | ||
| Notes | ||
| ----- | ||
| This implementation is inspired by the official one https://github.com/kwuking/TimeMixer | ||
| """ | ||
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| # Created by Wenjie Du <wenjay.du@gmail.com> | ||
| # License: BSD-3-Clause | ||
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| from .model import TimeMixer | ||
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| __all__ = [ | ||
| "TimeMixer", | ||
| ] |
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| @@ -0,0 +1,83 @@ | ||
| """ | ||
| """ | ||
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| # Created by Wenjie Du <wenjay.du@gmail.com> | ||
| # License: BSD-3-Clause | ||
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| import torch.nn as nn | ||
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| from ...nn.functional import ( | ||
| nonstationary_norm, | ||
| nonstationary_denorm, | ||
| ) | ||
| from ...nn.modules.timemixer import BackboneTimeMixer | ||
| from ...utils.metrics import calc_mse | ||
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| class _TimeMixer(nn.Module): | ||
| def __init__( | ||
| self, | ||
| n_layers, | ||
| n_steps, | ||
| n_features, | ||
| d_model, | ||
| d_ffn, | ||
| dropout, | ||
| top_k, | ||
| channel_independence, | ||
| decomp_method, | ||
| moving_avg, | ||
| downsampling_layers, | ||
| downsampling_window, | ||
| apply_nonstationary_norm: bool = False, | ||
| ): | ||
| super().__init__() | ||
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| self.apply_nonstationary_norm = apply_nonstationary_norm | ||
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| self.model = BackboneTimeMixer( | ||
| task_name="imputation", | ||
| n_steps=n_steps, | ||
| n_features=n_features, | ||
| n_pred_steps=None, | ||
| n_pred_features=n_features, | ||
| n_layers=n_layers, | ||
| d_model=d_model, | ||
| d_ffn=d_ffn, | ||
| dropout=dropout, | ||
| channel_independence=channel_independence, | ||
| decomp_method=decomp_method, | ||
| top_k=top_k, | ||
| moving_avg=moving_avg, | ||
| downsampling_layers=downsampling_layers, | ||
| downsampling_window=downsampling_window, | ||
| downsampling_method="avg", | ||
| use_future_temporal_feature=False, | ||
| ) | ||
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| def forward(self, inputs: dict, training: bool = True) -> dict: | ||
| X, missing_mask = inputs["X"], inputs["missing_mask"] | ||
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| if self.apply_nonstationary_norm: | ||
| # Normalization from Non-stationary Transformer | ||
| X, means, stdev = nonstationary_norm(X, missing_mask) | ||
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| # TimesMixer processing | ||
| dec_out = self.model.imputation(X, None) | ||
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| if self.apply_nonstationary_norm: | ||
| # De-Normalization from Non-stationary Transformer | ||
| dec_out = nonstationary_denorm(dec_out, means, stdev) | ||
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| imputed_data = missing_mask * X + (1 - missing_mask) * dec_out | ||
| results = { | ||
| "imputed_data": imputed_data, | ||
| } | ||
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| if training: | ||
| # `loss` is always the item for backward propagating to update the model | ||
| loss = calc_mse(dec_out, inputs["X_ori"], inputs["indicating_mask"]) | ||
| results["loss"] = loss | ||
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| return results |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,24 @@ | ||
| """ | ||
| Dataset class for the imputation model TimeMixer. | ||
| """ | ||
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| # Created by Wenjie Du <wenjay.du@gmail.com> | ||
| # License: BSD-3-Clause | ||
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| from typing import Union | ||
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| from ..saits.data import DatasetForSAITS | ||
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| class DatasetForTimeMixer(DatasetForSAITS): | ||
| """Actually TimeMixer uses the same data strategy as SAITS, needs MIT for training.""" | ||
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| def __init__( | ||
| self, | ||
| data: Union[dict, str], | ||
| return_X_ori: bool, | ||
| return_y: bool, | ||
| file_type: str = "hdf5", | ||
| rate: float = 0.2, | ||
| ): | ||
| super().__init__(data, return_X_ori, return_y, file_type, rate) |
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